Context Engineering Template

A comprehensive template for getting started with Context Engineering - the discipline of engineering context for AI coding assistants so they have the information necessary to get the job done end to end.

Context Engineering is 10x better than prompt engineering and 100x better than vibe coding.

🚀 Quick Start

# 1. Clone this template
git clone https://github.com/coleam00/Context-Engineering-Intro.git
cd Context-Engineering-Intro

# 2. Set up Codex project guidance
# Edit AGENTS.md (and nested files if needed) to capture your rules and workflows

# 3. Add examples (highly recommended)
# Place relevant code examples in the examples/ folder

# 4. Register Codex prompts
python scripts/install_codex_prompts.py --dry-run
python scripts/install_codex_prompts.py

# 5. Create your initial feature request
# Edit INITIAL.md with feature requirements and references

# 6. Generate a comprehensive PRP (Product Requirements Prompt)
# Run /generate-prp in Codex CLI or Cursor and provide the feature file path when prompted

# 7. Execute the PRP to implement your feature
# Run /execute-prp with the PRP path; follow the plan and validation gates

📚 Table of Contents

• What is Context Engineering?
• Template Structure
• Step-by-Step Guide
• Writing Effective INITIAL.md Files
• The PRP Workflow
• Using Examples Effectively
• Best Practices

What is Context Engineering?

Context Engineering represents a paradigm shift from traditional prompt engineering:

Prompt Engineering vs Context Engineering

Prompt Engineering:

• Focuses on clever wording and specific phrasing
• Limited to how you phrase a task
• Like giving someone a sticky note

Context Engineering:

• A complete system for providing comprehensive context
• Includes documentation, examples, rules, patterns, and validation
• Like writing a full screenplay with all the details

Why Context Engineering Matters

1. Reduces AI Failures: Most agent failures aren't model failures - they're context failures
2. Ensures Consistency: AI follows your project patterns and conventions
3. Enables Complex Features: AI can handle multi-step implementations with proper context
4. Self-Correcting: Validation loops allow AI to fix its own mistakes

Template Structure

context-engineering-intro/
├── AGENTS.md                 # Codex-native project guidance (authoritative)
├── codex/
│   ├── README.md             # Codex workflow quickstart
│   ├── config/
│   │   └── config.toml.example  # Sample Codex CLI config
│   └── prompts/
│       ├── execute-prp.md    # Slash prompt for PRP execution
│       └── generate-prp.md   # Slash prompt for PRP generation
├── PRPs/
│   ├── templates/
│   │   └── prp_base.md       # Base template for PRPs
│   └── EXAMPLE_multi_agent_prp.md  # Example PRP
├── examples/                 # Codex-focused examples and guides
├── scripts/
│   └── install_codex_prompts.py  # Prompt installer script
├── tests/
│   └── test_install_codex_prompts.py  # Installer test coverage
├── INITIAL.md                # Feature request template
├── INITIAL_EXAMPLE.md        # Example feature request
├── README.md                 # This file
├── PLANNING.md               # Architecture and planning scratchpad
└── TASK.md                   # Running log of completed work

This template doesn't focus on RAG and tools with context engineering because I have a LOT more in store for that soon. ;)

Step-by-Step Guide

1. Set Up Global Rules (AGENTS.md)

The AGENTS.md file contains project-wide rules that Codex CLI and Cursor follow in every conversation. The template includes:

• Project awareness: Reading planning docs, checking tasks
• Code structure: File size limits, module organization
• Testing requirements: Unit test patterns, coverage expectations
• Style conventions: Language preferences, formatting rules
• Documentation standards: Docstring formats, commenting practices

You can use the provided template as-is or customize it for your project. Include reminders for someone on the team to run /status at session start (the agent cannot trigger it automatically) and /plan on for multi-step work.

2. Create Your Initial Feature Request

Edit INITIAL.md to describe what you want to build:

## FEATURE:
[Describe what you want to build - be specific about functionality and requirements]

## EXAMPLES:
[List any example files in the examples/ folder and explain how they should be used]

## DOCUMENTATION:
[Include links to relevant documentation, APIs, or MCP server resources]

## OTHER CONSIDERATIONS:
[Mention any gotchas, specific requirements, or things AI assistants commonly miss]

See INITIAL_EXAMPLE.md for a complete example.

3. Generate the PRP

PRPs (Product Requirements Prompts) are comprehensive implementation blueprints that include:

• Complete context and documentation
• Implementation steps with validation
• Error handling patterns
• Test requirements

They are similar to PRDs (Product Requirements Documents) but are crafted more specifically to instruct an AI coding assistant.

Run in Codex CLI or Cursor:

/generate-prp

Note: Codex slash prompts live in codex/prompts/. Provide the target file path in the conversation when running the command because Codex CLI does not pass $ARGUMENTS automatically.

• codex/prompts/generate-prp.md documents the research and authoring workflow.
• codex/prompts/execute-prp.md defines the implementation loop and validation gates.

This command will:

1. Read your feature request
2. Research the codebase for patterns
3. Search for relevant documentation
4. Create a comprehensive PRP in PRPs/your-feature-name.md

4. Execute the PRP

Once generated, execute the PRP to implement your feature:

/execute-prp

Before executing, confirm the PRP file path is in the conversation so Codex can load it.

The AI coding assistant will:

1. Read all context from the PRP
2. Create a detailed implementation plan
3. Execute each step with validation
4. Run tests and fix any issues
5. Ensure all success criteria are met

Writing Effective INITIAL.md Files

Key Sections Explained

FEATURE: Be specific and comprehensive

• ❌ "Build a web scraper"
• ✅ "Build an async web scraper using BeautifulSoup that extracts product data from e-commerce sites, handles rate limiting, and stores results in PostgreSQL"

EXAMPLES: Leverage the examples/ folder

• Place relevant code patterns in examples/
• Reference specific files and patterns to follow
• Explain what aspects should be mimicked

DOCUMENTATION: Include all relevant resources

• API documentation URLs
• Library guides
• MCP server documentation
• Database schemas

OTHER CONSIDERATIONS: Capture important details

• Authentication requirements
• Rate limits or quotas
• Common pitfalls
• Performance requirements

The PRP Workflow

How /generate-prp Works

The command follows this process:

1. Research Phase

   • Analyzes your codebase for patterns
   • Searches for similar implementations
   • Identifies conventions to follow

2. Documentation Gathering

   • Fetches relevant API docs
   • Includes library documentation
   • Adds gotchas and quirks

3. Blueprint Creation

   • Creates step-by-step implementation plan
   • Includes validation gates
   • Adds test requirements

4. Quality Check

   • Scores confidence level (1-10)
   • Ensures all context is included

How /execute-prp Works

1. Load Context: Reads the entire PRP
2. Plan: Creates detailed task list using TodoWrite
3. Execute: Implements each component
4. Validate: Runs tests and linting
5. Iterate: Fixes any issues found
6. Complete: Ensures all requirements met

See PRPs/EXAMPLE_multi_agent_prp.md for a complete example of what gets generated.

Using Examples Effectively

The examples/ folder is critical for success. AI coding assistants perform much better when they can see patterns to follow.

What to Include in Examples

1. Code Structure Patterns

   • How you organize modules
   • Import conventions
   • Class/function patterns

2. Testing Patterns

   • Test file structure
   • Mocking approaches
   • Assertion styles

3. Integration Patterns

   • API client implementations
   • Database connections
   • Authentication flows

4. CLI Patterns

   • Argument parsing
   • Output formatting
   • Error handling

Example Structure

examples/
├── README.md           # Explains what each example demonstrates
├── cli.py             # CLI implementation pattern
├── agent/             # Agent architecture patterns
│   ├── agent.py      # Agent creation pattern
│   ├── tools.py      # Tool implementation pattern
│   └── providers.py  # Multi-provider pattern
└── tests/            # Testing patterns
    ├── test_agent.py # Unit test patterns
    └── conftest.py   # Pytest configuration

Best Practices

1. Be Explicit in INITIAL.md

• Don't assume the AI knows your preferences
• Include specific requirements and constraints
• Reference examples liberally

2. Provide Comprehensive Examples

• More examples = better implementations
• Show both what to do AND what not to do
• Include error handling patterns

3. Use Validation Gates

• PRPs include test commands that must pass
• AI will iterate until all validations succeed
• This ensures working code on first try

4. Leverage Documentation

• Include official API docs
• Add MCP server resources
• Reference specific documentation sections

5. Customize AGENTS.md

• Add your conventions
• Include project-specific rules
• Define coding standards

Resources

• OpenAI Codex CLI Documentation
• OpenAI Codex Quickstart
• Context Engineering Best Practices